2D Gaussian kernel coefficients calculating and plotting

gaussian.py

import collections
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D

def makeGaussian(size, sigma=None):
  if not isinstance(size, collections.Iterable):
    size = (size, size)
  w, h = size
  if sigma is None:
    sigma = 1.47 * min(w, h) / 6

  center = w / 2, h / 2
  x = np.arange(0, w, 1, np.float32) + 0.5
  y = np.arange(0, h, 1, np.float32) + 0.5
  y = y.reshape(-1, 1)
  
  kernel = np.exp(-1/2 * ((x - center[0])**2 + (y - center[1])**2)/(sigma**2))
  kernel = kernel / (sigma * (2 * np.pi)**0.5)
  kernel /= kernel.sum()
  return kernel

def plot(kernel):
  w, h = kernel.shape
  w_offset, h_offset = (1 - w % 2) / 2, (1 - h % 2) / 2
    
  fig = plt.figure()
  ax = fig.gca(projection='3d')
  X = np.arange(-w/2 + w_offset, w/2 + w_offset, 1)
  Y = np.arange(-h/2 + h_offset, h/2 + h_offset, 1)
  X, Y = np.meshgrid(X, Y)
  ax.plot_surface(X, Y, kernel, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
  
size = 6
kernel = makeGaussian(size)
print(kernel)
plot(kernel)